Net precipitation in burned and unburned subalpine forest stands after wildfire in the northern Rocky Mountains
Bibliographic record
Abstract
Wildfire can exert considerable influence on many watershed processes, including the partitioning of precipitation by forest canopies. Despite general acknowledgement that canopy interception is reduced following wildfire, effects on net rainfall and snow accumulation have not been quantified. The objectives of this study were to document net rainfall and snow water equivalent (SWE) in burned and unburned (reference) forest stands over a 10-year period to characterise the effects of severe wildfire on net precipitation in the Canadian Rocky Mountains. Differences in summer (June–September) rainfall between burned and reference stands suggest that wildfire reduced rainfall interception by 65%, resulting in a 48% increase in net rainfall from 2006 to 2008. This represented an average annual increase in net rainfall of 122 mm (36%) for 10 years after the fire. Similarly, a burned stand had 152 mm (78%) higher mean annual peak SWE than a paired reference stand. Collectively, burned stands had 274 mm (191–344 mm; 51%) more mean annual net precipitation for the first decade after fire. These results suggest that increases in net precipitation are likely following wildfire in subalpine forests and that, owing to the slow growth of these forests, post-fire changes may alter precipitation–runoff relationships for many years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".